Single-cell transcriptomics reveals a mechanosensitive injury signaling pathway in early diabetic nephropathy

Background Diabetic nephropathy (DN) is the leading cause of end-stage renal disease, and histopathologic glomerular lesions are among the earliest structural alterations of DN. However, the signaling pathways that initiate these glomerular alterations are incompletely understood. Methods To delineate the cellular and molecular basis for DN initiation, we performed single-cell and bulk RNA sequencing of renal cells from type 2 diabetes mice (BTBR ob/ob) at the early stage of DN. Results Analysis of differentially expressed genes revealed glucose-independent responses in glomerular cell types. The gene regulatory network upstream of glomerular cell programs suggested the activation of mechanosensitive transcriptional pathway MRTF-SRF predominantly taking place in mesangial cells. Importantly, activation of MRTF-SRF transcriptional pathway was also identified in DN glomeruli in independent patient cohort datasets. Furthermore, ex vivo kidney perfusion suggested that the regulation of MRTF-SRF is a common mechanism in response to glomerular hyperfiltration. Conclusions Overall, our study presents a comprehensive single-cell transcriptomic landscape of early DN, highlighting mechanosensitive signaling pathways as novel targets of diabetic glomerulopathy. Supplementary information The online version contains supplementary material available at 10.1186/s13073-022-01145-4.


Background
Diabetic nephropathy (DN) is one of the major complications in diabetic patients and is the leading cause of end-stage renal disease worldwide [1]. DN is a complex disease and its progression, particularly in type 2 diabetes, is confounded by multiple pathogenic factors, such as insulin resistance and obesity [1,2]. Previous studies used single-cell or single-nucleus RNA sequencing (scRNAseq or snRNA-seq) to investigate cellular changes or drug responses in diabetic kidneys. Single-nucleus transcriptomics on early human diabetic kidneys suggests increased potassium secretion in the distal nephron and pro-angiogenic signaling in diverse kidney cells [3]. A following study combining single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing highlights the glucocorticoid signaling in the proximal tubule [4]. Stefansson et al. provide molecular programs associated with glomerular hyperfiltration in human DN [5], and Fu et al. show the single-cell transcriptomics of eNOS − / − mice with streptozotocin-induced diabetes, which is a type 1 diabetes model without obesity nor insulin-resistance [6]. Single-cell/nucleus transcriptomics on drug-treated murine diabetic kidneys suggest important alterations in proximal tubule cells [7,8].
DN is characterized by distinct histopathological lesions dominating the renal glomerulus, such as glomerular basement membrane (GBM) thickening, mesangial expansion, and nodular glomerulosclerosis [2]. Early changes such as glomerular hyperfiltration have been proposed to be critical for the subsequent development of glomerulosclerosis [1]. Preservation of glomerular filtration rate is of great importance for renoprotection [2]. However, the signaling pathways that initiate these glomerular alterations are incompletely understood. Therefore, it is particularly warranted to understand the cellular changes and molecular mechanisms that initiate DN in the glomerulus.
In this study, we conducted a comprehensive analysis of cellular changes in type 2 diabetic glomeruli. The type 2 diabetes mouse model BTBR ob/ob exhibits insulin resistance, hyperglycemia, and obesity, as well as the rapid progression of diabetic glomerulopathy [9][10][11]. We generated single-cell transcriptomics of kidneys and bulk transcriptomics of purified glomerular cell types. We compared mouse DN with human DN and validated our findings in different patient cohort datasets at both mRNA and protein levels. Our study reveals the upregulation of the mechanosensitive signaling pathway MRTF-SRF in response to glomerular hyperfiltration, which is associated with glomerulopathy in early DN.

Animals
All animal experiments were conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals as well as the German law for the welfare of animals. Mice were housed in a specific pathogen-free facility with free access to chow and water and a 12-h/12-h day/night cycle. Breeding and genotyping were performed according to standard procedures.

Mouse kidney normothermic machine perfusion
All procedures involving mouse kidney perfusion described in this manuscript were conducted according to German and Hamburg law and approved by the veterinary administration of the City of Hamburg under the license N002/2020. As previously described [12], male C57BL/6 kidneys were collected under anesthesia (i.p. injection of 100 μL/10 g body weight of a solution containing 10 mg/ml Ketamine and 1.6 mg/ml Xylazine). Briefly, a laparotomy was performed, the aorta was cannulated, ligatures placed and closed around all main vessels, and perfusion initiated only in the right kidney. The left unperfused kidney was then removed and used as control. After this, the right kidney was removed from the mouse during ongoing perfusion. Perfusion took place ex vivo at 100 mmHg at 37 °C with 100% GBSS solution without the addition of albumin or red blood cells (Sigma G9779) continuously gassed in a dialyzer with 100% carbogen (95% O2, 5% CO2). Perfusion was stopped after 60 min. Isolation of glomeruli was performed as described in Additional file 1: Supplementary methods (FACS-sorted glomerular cells).

Pig kidney normothermic machine perfusion
Pig kidneys were collected from a German slaughterhouse. Kidneys were received directly after slaughter and immediately flushed with 400-500 ml 1 × PBS with 2 ml Heparin 10.000 IE/ml. Kidney were stored at + 4 °C for around 2 h, after which normothermic perfusion was initiated. Perfusion took place at 100 mmHg at 37 °C with increased glomerular pressure and hyperfiltration as described above. Renal cortex tissue was collected either before perfusion (control) or 120 min after start of perfusion (perfused).

Single-cell suspension
Glomeruli were enriched by depleting the PT segment from the kidneys using Percoll density gradient centrifugation described previously [13]. Briefly, the mouse was euthanized by cervical dislocation under anesthesia (4% isoflurane for 2 min). Both kidneys were harvested and the renal papilla of each kidney was removed. The renal tissue was minced into small pieces and transferred to a C tube (130-093-237, Miltenyi Biotec) filled with 5 ml of collagenase solution (1 mg/ml collagenase from Clostridium histolyticum [C9263, Sigma-Aldrich] and 0.25% bovine serum albumin [BSA] in DMEM/F-12 medium [11320074, Gibco]). The renal tissue was incubated at 37 °C for 5 min and homogenized using a gentleMACS ™ Dissociator (Miltenyi Biotec) with the program "m. spleen 01_01"; this process was repeated once. Subsequently, the renal tissue was passed through a 250-μm sieve and separated by Percoll density gradient centrifugation using a solution that contained 45% Percoll ® (GE17-0891-02, GE Healthcare) and 55% 2 × PBS-glucose at 17,500 rpm for 15 min at 4 °C. Red blood cells were removed with a Percoll density gradient. PTs that accumulated in the interphase of the gradient were discarded. The rest of the kidney tissue that accumulated in the upper phase of the gradient was taken, washed twice in ice-cold HBSS, and centrifuged at 400 × g for 4 min at 4 °C. A singlecell suspension was prepared using cold-active protease (CAP) as described in previous studies [14]. The pellet was suspended in 2 ml of CAP solution (10 mg/ml Bacillus licheniformis protease [P5380, Sigma-Aldrich], 5 mM CaCl 2 , 20 U/ml DNase [4716728001, Roche] in PBS) for 20 min on ice with repeated trituration steps for 20 s every 5 min. The digestion was neutralized by 15 ml PBS supplemented with 10% FBS and passed through a 40-μm Corning ® cell strainer. Cells were centrifuged at 400 × g for 4 min at 4 °C and washed twice in 20 ml PBS supplemented with 0.5% BSA. Dead cells were removed with a dead cell removal kit (130-090-101, Miltenyi Biotec) according to the manufacturer's protocol. The living cells were passed through a 30-μm cell strainer (04-004-2326, Sysmex), and the cell concentration was determined using a TC20 ™ automated cell counter (Bio-Rad). The cells were loaded onto a 10 × Genomics Chromium single-cell instrument. All steps were performed according to the standard protocol of the Chromium single-cell 3' v2/v3 kits to generate high-quality cDNA libraries.

Single-cell data analysis Preprocessing and quality control (QC) of scRNA-seq data
10 × Genomics raw sequencing data were processed using CellRanger software (version 3.0.2, 10 × Genomics, Pleasanton, CA), and the 10 × Genomics mouse genome mm10 3.0.0 release was used as the reference genome (function cellranger count). The matrices of cells and the unique molecular identifier (UMI) count were obtained and further processed with the R package Seurat (version 3.1.1) [15]. For QC, we first filtered out genes detected in fewer than 3 cells and data for cells in which fewer than 200 genes had nonzero counts. To remove potential doublets, cells with more than 7000 expressed genes (nFeature) were excluded. We removed low-quality cells with more than 50% mitochondrial genes among all detected genes, as is recommended for kidney tubular cells [16]. After clustering and cell type identification, we performed curated doublet and high-mitochondrial gene cell removal (see below) based on known lineagerestricted markers.

Dimensionality reduction and clustering
The Seurat R package (version 4.0.2) was used to perform unsupervised clustering analysis on scRNA-seq data. In brief, gene counts for cells that passed QC were normalized by library size and log-transformed (function NormalizeData, normalization.method = "LogNormalize", scale.factor = 10,000). Then, highly variable genes were detected (function FindVariableFeatures, selection.method = "vst", nfeatures = 2000). To reduce batch effects, we applied the integration method implemented in Seurat version 3 (functions FindIntegrationAnchors and IntegrateData, dims = 1:30). The integrated matrix was then scaled with the ScaleData function (default parameters). PCA was performed on the scaled data (function RunPCA, npcs = 30) to reduce dimensionality.
The number of principal components used for each clustering round was dataset dependent and was determined on the basis of the elbow of a PCA scree plot. The selected principal components were then used to compute the KNN graph based on the Euclidean distance (function FindNeighbors). Cell clusters were subsequently generated using the function FindClusters. The resolution of the FindClusters function for each dataset was also determined by the exploration of the top marker genes of each cluster. Uniform Manifold Approximation and Projection (UMAP) was used to visualize the clustering results. The top DEGs in each cluster were found using the FindAllMarkers function (min.pct = 0.25, logfc. threshold = 0.25) with Wilcoxon rank-sum tests. The most highly expressed genes were then used to determine the cell type of each cluster.

Curated doublet and high-mitochondrial gene cell removal
After the cell type was determined for clusters, we performed additional curated doublet and high-mitochondrial-cell removal. Based on the literature and the exploration of our datasets, we created a lineagerestricted marker gene list for tubular (Cubn, Epcam) and nontubular (Pecam1, Pdgfrb, Nphs2, Ptprc) cell types. We removed the cells that expressed markers of the opposite lineage. For nontubular cells, we discarded cells with more than 20% mitochondrial genes among all detected genes. There were 86,508 cells before curated doublet removal, 71,831 cells after curated doublet removal, and 770,944 cells after high-mitochondrial gene cell removal.

Differential gene expression analysis
The Seurat FindMarker function was used to perform differential gene expression analysis for each cell type between the control and DN groups. The test method used in the FindMarker function was MAST (v1.12.0) [17].

Single-nucleus data analysis Preprocessing and QC
10 × Genomics raw sequencing data were processed using CellRanger software (version 5.0.1, 10 × Genomics, Pleasanton, CA). Pig genome was built by function "cellranger mkref " using the Sscrofa v103 FASTA file and GTF file (https:// www. ensem bl. org) and then mapping was done and the count matrices were generated by function "cellranger count" with parameter "includeintrons. " For QC, firstly, we applied soupX [18] with default parameters to remove ambient RNA contamination. Then, the nuclei were filtered out if the number of genes detected was less than 500 or greater than 6000 or the percentage of mitochondrial genes detected exceeded 7%. To further remove potential doublets, Scrublet [19] was applied to the data and cells were excluded if they were identified as doublets (default parameters).

Dimensionality reduction and clustering
An updated version of Seurat R package (version 4.0.2) was used to perform unsupervised clustering analysis on snRNA-seq data since the experiments were performed later than the mouse scRNA-seq analysis. Count data normalization, scaling, highly variable gene selection, and sample integration were the same as the mouse single-cell data process described above. PCA was performed on the scaled data (function RunPCA, npcs = 30) and the first 15 PCs were used for clustering. The KNN graph was calculated (function FindNeighbors) and then the clustering result is obtained (function FindClusters, resolution = 0.5). To visualize the clustering result, UMAP coordinates were calculated (function RunUMAP, dims = 1:15). Wilcoxon method was used to perform the differential gene analysis. For each cluster, the marker gene list was determined by log2FoldChange > 0.25 and adj. p value < 0.05 (FindAllMarkers). The most highly expressed genes were then used to determine the cell type of each cluster.

Bulk RNA-seq Library preparation and RNA-seq
A small amount of RNA (2 ng) was used as input material, and libraries were prepared with a SMART-Seq Stranded Kit according to the user manual (Takara Bio USA, Mountain View, CA, USA). In brief, samples were fragmented at 85 °C for 6 min prior to firststrand synthesis. Illumina adaptors and indexes were added to single-stranded cDNA via 5 cycles of PCR. After library purification with AMPure beads and depletion of ribosomal cDNA with scZapR, final RNAseq library amplification (13 cycles) was conducted, and the final RNA-seq library was purified with AMPure beads. The library samples were quantified using Quant-iT PicoGreen dsDNA Reagent (Invitrogen; Thermo Fisher Scientific, Waltham, MA, USA) on a ClarioStar microplate reader according to the manufacturer's instructions (BMG LABTECH, Ortenberg, Germany). The quality, including fragment size, of the cDNA was assessed on an Agilent Technologies Bioanalyzer 2100 using an Agilent DNA 1000 kit according to the manufacturer's instructions (Agilent Technologies, Palo Alto, CA, USA).
Pooled samples were quantified with a Qubit 1X dsDNA HS Assay Kit on a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Single-read sequencing was performed on a NovaSeq 6000 device using an S2 Reagent kit (100 cycles) according to the manufacturer's instructions (Illumina Inc., CA, USA).

Bulk RNA-seq data analysis
The quality of the bulk RNA-seq reads was assessed using FastQC (v0.11.5), and the reads were aligned to the mouse reference genome (mm10) with Bowtie2 (v2.3.3.1) [20] using RSEM (v1.3.0) [21] with the default parameters. The function rsem-calculate-expression was used to align the reads and quantify the gene and isoform abundance. The output of rsem-calculate-expression separately gives the read count and transcripts per million (TPM) value for each gene and isoform. Differential expression analysis was carried out using gene read counts with the DESeq2 package (v1.22.2) [22] to produce log2FC values and corresponding p values and adjusted p values. Principal component analysis (PCA) was performed using regularized log transformation of the count data, and the results were visualized using gplots (v3.0.1.1).

Integrated analysis Correlation of bulk RNA-seq and scRNA-seq data
For podocytes, ECs, and mesangial cells, the single-cell data were correlated with bulk RNA-seq data. The cell type markers (top DEGs of each cell type identified by the Seurat FindAllMarkers function (min.pct = 0.25, logfc. threshold = 0.25) were used for correlation analysis. The normalized gene expression count matrices of cell type markers were extracted from the single-cell data and bulk RNA-seq data separately, and then Pearson correlation coefficients were calculated between every pair of single cells and replicates of bulk RNA-seq data.

CellChat
We applied Cellchat (v 1.4.0) [23] to infer cell-cell communications across all kidney cell types and the glomerular cell types. We used the mouse database curated in CellChat including the "Secreted Signaling, " "Cell-cell Contact, " and "ECM-Receptor. " The comparison analysis between diseased samples and control samples was performed according to the Cellchat tutorial (https:// htmlp review. github. io/? https:// github. com/ sqjin/ CellC hat/ blob/ master/ tutor ial/ Compa rison_ analy sis_ of_ multi ple_ datas ets. html). The communication probabilities were compared to identify the unregulated and downregulated signaling ligand-receptor pairs between the two conditions.

SCENIC
The gene regulatory network was inferred using pySCE-NIC (v 0.11.2, a lightning-fast python implementation of the SCENIC pipeline) [24,25]. Firstly, the GRNboost2 algorithm was used to infer gene regulatory network and generate co-expression modules (pyscenic grn). Next, the regulon prediction step was performed (pyscenic ctx, using the default parameters and mm10__refseq-r80__10kb_up_and_down_tss.mc9nr and mm10__refseq-r80__500bp_up_and_100bp_down_tss.mc9nr motif collections). Finally, the AUCell matrix was generated with a threshold of 0.01 (pyscenic aucell). Cell type-specific regulators were identified based on Z-score of the AUCell values for the cells of a given type.

Upstream analysis
We performed Qiagen's ingenuity Pathway Analysis (IPA) tool to identify transcriptional regulators (significance: p value < 0.05). Positive and negative z-score values indicated the activation or inhibition of transcriptional regulators, respectively.

Early diabetic nephropathy in Pima Indians
Protocol kidney biopsy specimens were collected from Pima Indians with type 2 diabetes. The study subjects participated in an intervention trial (Renoprotection in Early Diabetic Nephropathy in Pima Indians, clinicaltrials.gov, NCT00340678) [30,31]. Biopsies were obtained from patients after informed consent was obtained and with the approval of the local ethics committees. The analysis included gene expression profiles from DN patients (n = 68) and LD controls (n = 18). To account for ethical considerations, to ensure privacy protection, and to avoid identifying individual study participants in this vulnerable population of Pima people, the Institutional Review Board of the National Institute of Diabetes and Digestive and Kidney Diseases has stipulated that individual-level gene expression and genotype data from this study cannot be made publicly available.

Microarray analysis
Tissue processing and microdissection protocols were performed similarly on both ERCB and Pima biopsy material. Following renal biopsy, the tissue was transferred to an RNase inhibitor and microdissected into glomeruli and tubulointerstitial tissue. Total RNA was isolated, reverse-transcribed, and amplified. Fragmentation, hybridization, staining, and imaging were performed according to the Affymetrix Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA, USA). CEL file normalization was performed with the robust multichip average method using RMAExpress (Version 1.0.5) and the human Entrez-Gene custom CDF annotation from Brain Array version 18 (PIMA) and 25 (ERCB) (http:// brain array. mbni. med. umich. edu/ Brain array/ defau lt. asp). The log-transformed ERCB dataset was corrected for batch effect using ComBat from the GenePattern pipeline (http:// www. broad insti tute. org/ cancer/ softw are/ genep attern/). To identify DEGs, the Significance Analysis of Microarrays (SAM) method [32] was applied using the SAM function in Multiple Experiment Viewer (TiGR MeV, Version 4.9). A q-value below 5% was considered to indicate statistical significance.

Immunofluorescent staining
Paraffin sections from human biopsies or from experimental mice (3 μm thick) were deparaffinized and rehydrated in water. Following antigen retrieval with DAKO A piece of pig cortex (1 cm deep) was snap frozen in OCT. Seven-micrometer cryo-sections were fixed immediately in 4% PFA for 10 min at RT and washed out with PBS. The sections were incubated for 30 min with 5% BSA in PBS + 0.1% Triton for blocking and permeabilization and then incubated overnight at 4 °C with primary antibody MEIS1 (Invitrogen MA5-27,191). After washing out the primary antibody, sections were incubated for 1 h at RT with secondary antibody (Life Technologies) and DAPI (4′,6-diamidino-2-phenylindole) (Invitrogen). Slides were mounted and analyzed using the Leica TCS SP5 microscope.

scRNA-seq of BTBR ob/ob mouse kidneys with early DN
We generated BTBR ob/ob podocyte-reporter mice (BTBR.Cg-Lep ob/ob WiscJ ;Gt (ROSA)26Sor tm4(ACTB−tdTomato,−EGFP)Luo ;Tg(NPHS2-cre) 295Lbh ; hereafter referred to as BTBR ob/ob or DN mice) to investigate the cellular and molecular changes that occur in DN. By 6 weeks of age, both male and female BTBR ob/ob mice exhibited obesity, hyperglycemia, and albuminuria (Additional file 1: Figure S1a). From 12 weeks of age, obvious glomerular hypertrophy was observed in histological analysis (Additional file 1: Figure S1b). Mesangial expansion with accumulation of collagen IV was detected in the DN glomeruli (Additional file 1: Figure S1c and d). Based on these observations, we defined 6 weeks of age as the onset stage of DN and 12 weeks of age as the early stage of DN.
Kidney tissue was sampled from a total of 16 mice, including male and female each of BTBR ob/ob (DN) and BTBR WT (control) mice at 6 and 12 weeks of age. The samples were partially depleted of the proximal tubule (PT) segment using a Percoll gradient [13] to enrich glomeruli. Single-cell suspensions were prepared using cold-active protease (CAP) to reduce dissociationinduced artifacts in kidney cells [33]. A total of 70,944 single cells were profiled after data pre-processing and quality control (Fig. 1b). Unsupervised clustering identified 21 clusters (Additional file 1: Figure S2a, b and c; see the "Methods" section). The cells in the 21 clusters were classified into 18 cell types and annotated on the basis of cell-specific marker genes reported in previous kidney and glomerulus single-cell data [14][15][16]. Defining marker genes for each cell type and unbiased marker genes for each cluster are demonstrated in a dot plot (Fig. 1c) and in a heatmap (Additional file 1: Figure S2d), respectively.
We identified all major components in the kidneys. The endothelial cell (EC) subtypes expressed distinct marker genes, such as Ehd3 for EC_1 (glomerular ECs/ gECs), Plvap for EC_2 (fenestrated ECs with diaphragm in veins and peritubular capillaries), and Fbln2 for EC_3 (arteriolar and arterial ECs) [34]. Mesangial cells defined by marker genes Gata3, Itga8, and Adamts5 are clearly distinguished from interstitial cells, which were composed of vascular smooth muscle cells marked by Acta2 and Tagln, and renin cells marked by Ren1 [35,36]. The subtype PT_1 and PT_2 were composed of cells from S1/2 and S2/3 PT segments, respectively, as they showed a continuum of marker gene expression consistent with known S1 (Slc5a2, Slc5a12 and Gatm) to S3 (Slc13a3, Cyp4a10, and Slc5a8) PT segments.
Due to PT depletion, the percentage of PT cells was reduced to 22.4%, while glomerular cell types were enriched to 15.6% (Additional file 1: Figure S2e). The fractions of control and DN cells in each cell type were similar except for podocytes, which included more control (approximately 77%, 807 cells) than DN cells (approximately 23%, 235 cells) (Additional file 1: Figure S2f ). All cell types were identified in individual kidney samples (Additional file 1: Figure S3a), and cells from individual kidney samples were also identified for each cell type (Additional file 1: Fig. S3b). The overall low expression of dissociation-induced stress genes in individual cell types (Additional file 1: Fig. S3c) indicates the advantages of our modified single-cell preparation protocol (Methods), which shortened the 37 °C digestion time and omitted the steps stressful to glomerular cell dissociation.

Cell type-specific alterations in DN kidneys suggest glucosedependent and glucose-independent responses in diabetes
Next, we compared gene expression levels in DN versus control kidneys by cell type. In total, we identified 447 and 740 differentially expressed genes (DEGs) across all cell types at 6 and 12 weeks, respectively (Fig. 1d). Among all cell types, mesangial cells and PT cells had the highest numbers of DEGs (Fig. 1d), indicating that these two cell types were predominantly affected in the early stage of DN. Enrichment analysis identified significant pathways in glomerular cell types and PT cell types at 6 and 12 weeks (Fig. 1e and f ). Glomerular cell types showed regulations of extracellular matrix molecules, cell adhesion, cell proliferation, etc., and cellular responses to mechanic stress, cytokines, and blood pressure. PT cell types showed multiple metabolic processes such as biological oxidations, and responses involved in oxidative stress, such as NRF2 and PPAR signaling pathways. These differences suggest glucose-dependent and glucose-independent responses in PT and glomerular cell types in early DN, respectively.

Shared features of human and experimental DN
Early changes such as glomerular hypertrophy, GBM thickening, and mesangial expansion occur in both human and animal DN [37]. Thus, we conducted intensive analyses on glomerular cell types.
We isolated glomeruli from BTBR ob/ob and BTBR WT kidneys and subsequently purified podocytes, mesangial cells, and gECs by fluorescence-activated cell sorting (FACS) (Additional file 1: Figure S4, Additional file 1: Supplementary methods). Bulk RNA-seq was performed for each purified glomerular cell type. The similarity and purity of individual glomerular cell type samples were controlled by principal component analysis (PCA) and the expression levels of specific marker genes (Additional file 1: Figure S5). The cell gene expression data from scRNA-seq were compared to the bulk RNA-seq data. Podocytes, EC_1/gECs, and mesangial cells showed positive Pearson correlation coefficients, suggesting a linear correlation of the two datasets (Fig. 2a). Overlapping the glomerular DEGs from single-cell and bulk RNA-seq datasets resulted in 194 unique genes (Fig. 2b), which were mapped to the European Renal cDNA Bank (ERCB) patient dataset. The mRNA expression of 106 genes was significantly regulated in microdissected glomeruli from DN patients (Fig. 2b and c). Importantly, most DEGs detected in human DN glomeruli were significantly changed in mesangial cells in both single-cell and bulk RNA-seq data (Fig. 2c).

Mechanosensitive transcriptional regulators are activated in DN glomeruli
To identify the transcriptional regulations responsible for the differential expression of genes in DN glomerular cell types, we performed upstream analysis using Ingenuity Pathway Analysis (IPA), which identifies not only transcription factors but also transcription coregulators such as coactivators. Transcriptional regulators commonly changed in single-cell and bulk RNA-seq data are shown in Fig. 3a. Changes in transcriptional regulations were predominant in mesangial cells. Intriguingly, we observed the activation of mechanosensitive transcriptional regulators, including coactivators myocardinrelated transcription factor A and B (MRTFA/B) and yes-associated protein 1 (YAP1 or YAP), as well as the transcription factor serum response factor (SRF). Additionally, the gene regulatory network was inferred using SCENIC [24,25] to determine the cell-type specific activity of transcription factors (Additional file 1: Figure S6b). Consistently, the activity of SRF was high in mesangial cells and moderate or low in podocytes and gECs, respectively (Fig. 3b). The MRTF-SRF transcriptional target genes with significant regulations in DN glomerular cell types are shown in Fig. 3c. Notably, the MRTF-SRF-specific targets Acta2 and Tagln [38] were found in both single-cell and bulk RNA-seq data. Enrichment analysis of significantly changed MRTF transcriptional target genes suggested regulations of smooth muscle proliferation, actin cytoskeleton, integrin cell surface interactions, and extracellular matrix organization (Additional file 1: Figure S6c).
MRTF-SRF is an important mechanosensitive transcriptional pathway in cells [39]. MRTF family consists of myocardin, MRTFA (MKL1, MAL), and MRTFB (MKL2). Unlike myocardin, which is limited to myocardial cells and smooth muscle cells, MRTFA and MRTFB are found in various cells and tissues including the kidney [40]. Mechanical stresses lead to the translocation of cytoplasmic MRTF into the nucleus to initiate the SRFmediated transcriptional responses [40]. We investigated the expression and location of MRTFA and MRTFB in mouse DN by immunofluorescent staining. MRTFA and MRTFB were found in the cytoplasm and nuclei of podocytes and gECs, exhibiting comparable expression levels in both control and DN mice (Fig. 3d). Mesangial cells exhibited a low expression level of MRTFA in control mice, while some of the mesangial cells exhibited nuclear accumulation of MRTFA in DN mice (Fig. 3d). MRTFB expression was absent in most mesangial cells in control mice, but its expression level was clearly increased in DN mice. Importantly, nuclear accumulation of MRTFB in mesangial cells was obvious to detect especially in the region where mesangial expansion was taking place (Fig. 3d).

MRTF transcriptional target genes are upregulated in DN patients with type 2 diabetes
We investigated the expression of MRTF transcriptional target genes in an early DN cohort of Pima Indians from the Gila River Indian Community in Arizona, individuals with type 2 diabetes [31]. mRNA expression analysis of microdissected glomeruli showed that various genes significantly changed in mouse DN were also significantly regulated in early human DN (Fig. 4a). Many of these target genes were positively correlated with mesangial volume (Fig. 4b). Independent single-nucleus transcriptomics data from early human DN [3] showed that these MRTF transcription genes were mostly regulated in mesangial cells (Additional file 1: Figure S7a). The MRTF transcriptional target genes were mapped in the ERCB dataset. These genes were significantly regulated in glomeruli in the context of DN and other diseases potentially causing mechanical stresses, such as arterial hypertension, as well as various other glomerular diseases with glomerulosclerosis, such as lupus nephritis (caused by systemic lupus erythematosus, SLE) and IgA nephropathy, but not in minimal change disease (MCD) (Fig. 4c).
Next, we investigated the expression and location of MRTFA and MRTFB in early human DN by immunofluorescent staining. A basal and comparable expression level of MRTFA was detected in all three glomerular cell types in both healthy and early DN kidney biopsy samples (Fig. 5). Consistent with the observations in mouse DN, MRTFB was absent in most mesangial cells from healthy individuals but was accumulated in the nuclei of mesangial cells where glomerular lesions were observed (Fig. 5). Taken together, our immunofluorescent staining analyses on human and mouse DN confirm the activation of MRTFB in mesangial cells during early DN.

Mesangial cells exhibit dominant signaling networks in DN glomeruli
Our results suggested that MRTF-SRF transcriptional regulation was activated in both mouse and human DN, predominantly in mesangial cells. Additionally, we identified various mesangial cell marker genes that encode transmembrane proteins responsible for the perception and transduction of mechanical signals, including mechanosensitive ion channels (MSCs), GPCRs, integrins, and cadherins [39] (Fig. 6a). Furthermore, we performed CellChat [23] to study the changes of intercellular communications (secreted signaling, cell-cell contact, and ECM-receptor) taking place in DN. We compared interaction strength between all kidney cell types in DN versus control mice, which showed dominating interactions of mesangial cells with all other cell types in DN kidneys (Fig. 6b). Additionally, mesangial cells dominated the intercellular communications (Fig. 6c) and interacted as a sender extensively with gECs and podocytes (Fig. 6d) in DN glomeruli. These interactions involved various well-known growth factor signaling pathways associated with the pathogenesis of chronic kidney disease (CKD), such as bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and vascular endothelial growth factor (VEGF), as well as interactions between multiple extracellular matrix proteins and integrins. Notably, the BMP2/4 signaling pathways identified in DN mesangial cell interactions contribute to glomerulosclerosis and tubulointerstitial fibrosis, particularly in DN [41].

Kidney ex vivo perfusion activates mechanosensitive signaling pathways
Glomerular hyperfiltration as a consequence of increased single-nephron glomerular filtration rate may be a common upstream mechanism contributing to CKD [2]. In diabetes, glomerular hyperfiltration is driven by the sodium-glucose cotransporter 2 (SGLT2) [2]. Hyperreabsorption of glucose and sodium in the PT ultimately leads to restricted tubuloglomerular feedback, resulting in increased intraglomerular pressure and glomerular hyperfiltration [2]. Consequently, circumferential and axial capillary wall stress, as well as fluid shear stress at the glomerular filtration barrier, is increased [42]. Since the mechanosensitive signaling pathway MRTF-SRF is a central coordinator of fibrosis-relevant mechanoresponse [39], we hypothesize that glomerular hyperfiltration may directly activate MRTF-SRF, leading to cellular changes associated with diabetic glomerulopathy. We used normothermic machine perfusion to prove this hypothesis. The ex vivo kidney perfusion takes place for 1 to 2 h at 100 mmHg with 100% Gey's Balanced Salt Solution (GBSS) without the addition of albumin or red blood cells (see the "Methods" section). Under this condition, tubular glomerular feedback is absent and 100 mmHg is a supraphysiologic pressure leading to glomerular hyperfiltration [43].
We generated bulk and single-nucleus transcriptomics data of isolated mouse glomeruli and pig renal cortex tissue, respectively (Fig. 7a, d), and gene expression was compared in perfused versus unperfused conditions. In ex vivo perfused mouse glomeruli, upstream analysis in DEGs suggested activation of MRTFA/B, SRF, and YAP1 (Fig. 7b). Enrichment analysis of significantly changed MRTF transcriptional target genes revealed actin cytoskeleton organization, VEGFA-VEGFR2 signaling pathway, smooth muscle contraction, PDGFRB pathway, and cellular response to external stimulus (Fig. 7c).
In ex vivo perfused pig kidney, a total of 11,692 single nuclei were profiled from perfused and unperfused renal cortex tissue. Unsupervised clustering identified 18 clusters (Additional file 1: Figure S7b), which were classified into 12 cell types ( Fig. 7e; Additional file 1: Figure S7c) on the basis of cell/nucleus-specific marker genes reported for previous human kidney single-nucleus data (Fig. 7f ) [3,36]. We identified the major glomerular cell types including a stromal cell type (indicated by "STROMA" in figures) defined by the nucleus marker gene MEIS1 and FHL2 (Fig. 7f ) [44]. Immunofluorescent staining on the pig renal cortex showed that MEIS1-positive cells were mainly composed of mesangial cells, but also some interstitial cells (Fig. 7g). Upstream analysis on DEGs revealed the activation of MRTFA/B, SRF, and YAP1 in stromal cells, as well as the activation of YAP1 and MRTFB in podocytes and endothelial cells, respectively (Fig. 7h). Significantly regulated MRTF transcriptional target genes were found predominantly in stromal cells (Fig. 7i). These findings support our hypothesis that the activation of mechanosensitive signaling pathways is a common mechanism in response to glomerular hyperfiltration.

Discussion
BTBR ob/ob is a useful mouse model to investigate cellular changes and molecular mechanisms in early DN. Integrative analysis of single-cell and bulk RNA-seq data led to the identification of the mechanosensitive pathway MRTF-SRF in DN glomeruli. The association of MRTF-SRF activation with human DN was validated in independent patient cohorts at both mRNA and protein levels, and MRTF-SRF is suggested as a common mechanism in response to glomerular hyperfiltration. Single-cell analysis on BTBR ob/ob kidneys suggested that PT cells and mesangial cells are the most sensitive cells to hyperglycemia and initiate glucose-dependent and glucose-independent downstream pathways in response to stress in the kidneys, respectively. PT cells showed adaptations to glucose metabolic fluxes and upregulation of antioxidant pathways. Increased glucose influx in cells fuels oxidative phosphorylation, leading to the overproduction of superoxide by mitochondria and the generation of reactive oxygen species, which play a central role in initiating diverse pathways responsible for diabetic abnormalities such as cellular dysfunction, inflammation, and fibrosis [1]. Mesangial cells showed the activation of other signaling pathways obviously independent of glucose metabolic flux. This finding is consistent with those of previous studies showing that overexpression of GLUT1 in mesangial cells exposed to high glucose fails to induce TGF-β1 synthesis and oxidative stress [45] and that transgenic mice with GLUT1 overexpression in mesangial cells do not develop pathological phenotypes until 26 weeks of age [46].
Mouse DN glomeruli exhibited shared features with human DN glomeruli. Approximately 70% glomerular DEGs identified in DN mice were also significantly regulated in DN patients. Importantly, MRTF-SRF transcriptional target genes were significantly regulated in both mouse and human DN glomeruli. Notably, mesangial cells responded at the earliest time point and showed a persistent activation of MRTFB compared to podocytes and gECs. Immunofluorescent staining further confirmed the activation of MRTFB in the region where mesangial expansion was taking place. Mechanic stimulation triggers cytoskeleton remodeling, resulting in the activation of mechanosensitive transcriptional pathway MRTF-SRF [39], which plays a key role in organ fibrosis [39,47]. Upon stimulation, cytoplasmic MRTF translocate to the nucleus and interact with SRF to target the expression of muscle-specific and contractile genes, as well as extracellular matrix genes [48,49]. Interestingly, single-cell data showed mesangial cell expression of numerous genes that encode transmembrane proteins responsible for the perception and transduction of mechanical signals. DN mesangial cells exhibited a dominant signaling network in the whole kidney, interacted with gECs and podocytes by ECM, and secreted signaling. These results suggest that MRTF-SRF transcriptional regulation in mesangial cells could be an important pathological pathway contributing to diabetic glomerulopathy. Since the mechanical stress caused by glomerular hyperfiltration is thought to play a critical role in the development of glomerulopathy [42], we speculate that glomerular hyperfiltration, a key event in early DN [1,2], directly activates mechanosensitive signaling pathways in renal glomeruli, and consequently contribute to diabetic glomerulopathy. This speculation is supported by evidence in our study. First, single-cell and bulk RNA-seq of DN mice showed activation of MRTFA/B in three major glomerular cell types, which was also observed in an early DN cohort. Second, ex vivo kidney perfusion-induced glomerular hyperfiltration showed a prominent activation of MRTFA/B, SRF, and YAP1 in mouse glomeruli. Third, snRNA-seq of perfused pig renal cortex tissue showed activation of these mechanosensitive transcriptional regulators, which were most prominently activated in stromal cells composing mesangial cells.
Many MRTF target genes were significantly regulated not only in DN but also in the context of other glomerular diseases associated with glomerulosclerosis. These findings suggest a common role of MRTF in diabetic glomerulopathy, as well as in glomerulosclerosis generally. Previous studies demonstrated that MRTF-SRF and YAP are essential for podocyte structure and function [50,51]. Tubulointerstitial fibrosis was diminished in MRTFAdeficient mice and MRTFA was activated in high-fat dietand streptozotocin-induced DN [52]. In addition, genetic deletion or pharmacological inhibition of MRTF attenuates fibrosis in various pathological conditions, including kidney diseases like diabetic nephropathy, obstructive nephropathy, acute kidney injury, and polycystic kidney disease [47]. Therefore, MRTF is important for glomerular cells and is a potential therapeutic target for DN, as well as glomerulosclerosis.
Finally, while the principle of SGLT2 inhibition has been proven to be the major advance in slowing down CKD, the mechanisms remain unclear [2]. It was proposed that a lowering of the intraglomerular pressure by SGLT2 inhibition might drive the renal protective effects. Our results unravel now mechanosensitive signaling pathways as possible mediators of SGLT2-dependent effects, opening the search for novel druggable downstream targets of SGLT2.

Conclusions
Our study presents a comprehensive single-cell transcriptomic landscape of early DN and reveals cell-specific alterations in gene expression that occur with DN onset and progression. Our intensive analysis of glomerular cell types reveals that mechanosensitive signaling is associated with diabetic glomerulopathy and could play a driving role in response to glomerular hyperfiltration. The MRTF transcription pathway may be part of a common mechanism in the glomerulus in the context of glomerulosclerosis.